Resource requests prediction in the cloud computing environment with a deep belief network

Summary Accurate resource requests prediction is essential to achieve optimal job scheduling and load balancing for cloud Computing. Existing prediction approaches fall short in providing satisfactory accuracy because of high variances of cloud metrics. We propose a deep belief network (DBN)‐based a...

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Bibliographic Details
Published in:Software, practice & experience Vol. 47; no. 3; pp. 473 - 488
Main Authors: Zhang, Weishan, Duan, Pengcheng, Yang, Laurence T, Xia, Feng, Li, Zhongwei, Lu, Qinghua, Gong, Wenjuan, Yang, Su
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.03.2017
Subjects:
Accuracy
Belief networks
Cloud computing
deep belief network
Design analysis
Design parameters
Mathematical models
Optimization
Parameters
prediction
resource request
ISSN:0038-0644, 1097-024X
Online Access:Get full text
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Summary:Summary Accurate resource requests prediction is essential to achieve optimal job scheduling and load balancing for cloud Computing. Existing prediction approaches fall short in providing satisfactory accuracy because of high variances of cloud metrics. We propose a deep belief network (DBN)‐based approach to predict cloud resource requests. We design a set of experiments to find the most influential factors for prediction accuracy and the best DBN parameter set to achieve optimal performance. The innovative points of the proposed approach is that it introduces analysis of variance and orthogonal experimental design techniques into the parameter learning of DBN. The proposed approach achieves high accuracy with mean square error of [10−6,10−5], approximately 72% reduction compared with the traditional autoregressive integrated moving average predictor, and has better prediction accuracy compared with the state‐of‐art fractal modeling approach. Copyright © 2016 John Wiley & Sons, Ltd.
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ISSN:0038-0644
1097-024X
DOI:10.1002/spe.2426